Multimodal geometric learning for antimicrobial peptide identification by leveraging alphafold2-predicted structures and surface features

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dc.contributor.authorSun, Z.
dc.contributor.authorXu, J.
dc.contributor.authorZhang, Y.
dc.contributor.authorZhang, Y.
dc.contributor.authorWang, Z.
dc.contributor.authorWang, X.
dc.contributor.authorLi, S.
dc.contributor.authorGuo, Y.
dc.contributor.authorShen, H.H.
dc.contributor.authorSong, J.
dc.date.issued2025
dc.description.abstractAntimicrobial peptides (AMPs) are short peptides that play critical roles in diverse biological processes and exhibit functional activities against target organisms. While numerous methods have demonstrated the effectiveness of deep neural networks for AMP identification using sequence features; nevertheless, higher-level peptide characteristics—such as 3D structure and geometric surface features—have not been comprehensively explored. To address this gap, we introduce the SSFGM-Model (Sequence, Structure, Surface, Graph, and Geometric-based Model), a novel framework that integrates multiple feature types to enhance AMP identification. The model represents each peptide sequence as a graph, where nodes are characterized by amino acid features derived from ProteinBERT, ESM-2, and One-hot embeddings. Graph convolutional networks and an attention mechanism are employed to capture high-order structural and sequential relationships. Additionally, surface geometry and physicochemical properties are processed using a geometric neural network. Finally, a feature fusion strategy combines the outputs from these subnetworks to enable robust AMP identification. Extensive benchmarking experiments demonstrate that the SSFGM-Model outperforms current state-of-the-art methods. An ablation study further confirms the critical role of sequence, structural, and surface features in AMP identification. The key contribution of this work is the innovative integration of multiple levels of peptide characteristics and the combination of geometric and graph neural networks. This approach provides a more comprehensive understanding of the sequence-structure–function relationship of peptides, paving the way for more accurate AMP prediction. The SSFGM-Model has a significant potential for applications in the discovery and design of novel AMP-based therapeutics. The source code is publicly available at https://github.com/ggcameronnogg/SSFGM-Model.
dc.description.statementofresponsibilityZehua Sun, Jing Xu, Yumeng Zhang, Yiwen Zhang, Zhikang Wang, Xiaoyu Wang, Shanshan Li, Yuming Guo, Hsin Hui Shen, Jiangning Song
dc.identifier.citationBriefings in Bioinformatics, 2025; 26(3):bbaf261-1-bbaf261-13
dc.identifier.doi10.1093/bib/bbaf261
dc.identifier.issn1467-5463
dc.identifier.issn1477-4054
dc.identifier.orcidXu, J. [0000-0002-8711-9199]
dc.identifier.urihttps://hdl.handle.net/2440/147858
dc.language.isoen
dc.publisherOxford University Press
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1127948
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1144652
dc.rights© The Author(s) 2025. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site-for further information please contact journals.permissions@oup.com.
dc.source.urihttps://doi.org/10.1093/bib/bbaf261
dc.subjectantimicrobial peptides; sequence features; surface features; structure information; geometric learning; AlphaFold2; graph neural network
dc.titleMultimodal geometric learning for antimicrobial peptide identification by leveraging alphafold2-predicted structures and surface features
dc.typeJournal article
pubs.publication-statusPublished

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